Well here is a bit of an issue. In order to compare baseline vs incremental demand in the period being examined (May 2016) one would also have to strip the incremental from the baseline in the preceding periods that are being compared. That's what makes this kind of analysis exceptionally tricky/impossible, especially when we're looking at monthly aggregate extrapolated data.
I'm not sure what you mean specifically here. Maybe explain the methodology you are thinking about a bit more. It sounds like you are saying we should take the incrementally boosts figure and subtract from it the baseline figure (which is what I was advocating), so I must not be understanding your diction here unless you mean to agree with me, heh.
Yeah, problem is May 2015 isn't really comparable, until you strip out incremental and then adjust for cyclicality. It's a real tough comparison to try and do.
Again, not sure what you mean here. And *anything* is comparable. I can compare any two months to one another and draw conclusions from them. The set of rigorous conclusions I derive may not be very interesting, but conclusions can certainly be drawn regardless. Lemme try this again, this time by posing a question:
I want to know how large a unit increase PS4 got thanks to conditions XYZ. How would you address answering this query?
Are you saying that we should look somewhere else other than YoY comparisons here to address the above question? If so, that's fine. But it also changes the question I was using to frame the analysis earlier in the thread, which is what I was debating. I would agree that we can debate/discuss different strategies for deciding on 'where PS4 would have been without conditions XYZ present' and that is a critical consideration to be made fwiw.
I think I'd agree with you that the % isn't a great measure in this case, but really, I'd extend to the actual unit difference as well. The variance from the expectation isn't significantly higher, it's not outside what an expected range of error would capture.
I didn't note anything about standard deviations or confidence levels, etc in my posed example. Assume the standard deviation is 1 unit for the sake of my extreme example. You may find that extreme, but the example is supposed to carrying his logic out to its extremes so that shouldn't be problematic.
Yes, agree with you on the big/medium/small and assigning qualitative measures (impressive/not impressive) without quantitative framing is frustrating/pointless. As for the number itself, coming in over 200k was a good result, but I wasn't thinking that it was exceptionally so. More in the range of, "yeah, okay, that's alright". I don't know. I don't do qualitative assessments very well lol.
I'd add that I agree 200k is a great result for PS4 sales. I just don't find a 50k boost to be impressive for those particular conditions all working hard to favor PS4 sales.
I think both % and unit changes are important and meaningful, but I think they're only truly meaningful when placed in context with each other.
I'd say % is only meaningful
iff partnered with the unit increase (or a way to calculate the unit increase). I'd also say the unit difference is the penultimate metric for addressing the question I posed, which seems to me a perfectly valid question to pose when trying to quantify a boost, let alone judge it. So some slight disagreement here.
And I very much prefer the baseline/incremental demand methodology for measuring sales of consumer goods. It's just that the data we have to work with is such absolute shit for doing anything but the most rudimentary analytics that it makes the exercise laughable in practice.
Sure, but I'm not sure asking about what kind of boost PS4 enjoyed relative to last May requires anything beyond rudimentary analytics.
